Circular regional mean completed local binary pattern for texture classification

Abstract

The local binary pattern (LBP) is a simple yet efficient texture operator, and the completed local binary pattern (CLBP) is a completed modeling for LBP that has been adopted in many texture classification methods. However, existing CLBP operators are sensitive to noise and they cannot extract the regional structure information efficiently. To overcome these disadvantages, we propose a circular regional mean completed local binary pattern (CRMCLBP) by introducing a circular regional mean operator to modify the traditional CLBP. We also present two encoding schemes for CRMCLBP. The proposed CRMCLBP not only achieves rotation invariance and completed representation capability but also has high robustness to image noise. In order to evaluate the performance, we compare the CRMCLBP with recent state-of-the-art methods by extensive experiments on two popular texture databases including Outex database and Columbia-Utrecht reflection and texture database. Excellent experimental results demonstrate that the proposed CRMCLBP is comparable with recent state-of-the-art texture descriptors and superior to other approaches for robustness.